Pulmonologist performed ultrasound guided fine needle aspiration of lung lesions
Bibliographic record
Abstract
Abstract Background and objective: Lung cancer is increasingly common and accurate diagnosis is important for personalized treatment. Ultrasound guided percutaneous fine needle aspiration is a useful method to obtain specimen for histological diagnosis of peripheral lung lesions. The aim of this study is to evaluate the diagnostic accuracy and complication rate of the procedure performed by pulmonologists. The result is compared with ultrasound guided core needle biopsy performed by radiologists. Methods: We retrospectively evaluated the diagnostic accuracy and complication rate of pulmonologist-performed ultrasound guided FNA of lung lesions in the period of 1st August 2019 to 30th June 2021 (pulmonologist group) and radiologist-performed ultrasound guided CNB of lung lesions in the period of 1st January 2010 to 31st December 2014 (radiologist group). Logistic regression analysis was used to identify independent influence factors associated with diagnostic accuracy in pulmonologist group and in combining both groups. Results: In a 23-month period, pulmonologists in a tertiary center performed 113 episodes of ultrasound guided fine needle aspiration for peripheral lung lesions. The diagnostic accuracy and complication rate were 80.4% and 5.3% respectively, comparable to 86.8% and 7.4% in a historical cohort consisting of 68 episodes of ultrasound guided core needle biopsy performed by radiologists in the same hospital. Lung lesions of upper lobe location were predictive of successful diagnosis. Conclusion: Ultrasound guided fine needle aspiration by pulmonologist is an easily accessible and reliable method to obtain specimen for histological diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".